Structure Aware Image Downscaling

Abstract: Image downscaling is one of the key operations in recent display technology and visualization tools. By this process, the dimension of an image is reduced, aiming to preserve structural integrity and visual fidelity. In this paper, we propose a new image downscaling method which is built on the core ideas of image filtering and edge detection. In particular, we present a structure-informed downscaling algorithm that maintains fine details through edge-aware processing. The proposed method comprises three steps: (i) edge map computation, (ii) edge-guided interpolation, and (iii) texture enhancement. To faithfully retain the strong structures in an image, we first compute the edge maps by applying an efficient edge detection operator. This is followed by an edge-guided interpolation to preserve fine details after resizing. Finally, we fuse local texture enriched component of the original image to the interpolated one to restore high-frequency information. By integrating edge information with adaptive filtering, our approach effectively minimizes artifacts while retaining crucial image features. To demonstrate the effective downscaling capability of our proposed method, we validate on four datasets: DIV2K, BSD100, Urban100, and RealSR. For downscaling by 4x, our method could achieve as high as 39.07 dB PSNR on the DIV2K dataset and 38.71 dB on the RealSR dataset. Extensive experimental results confirm that the proposed image downscaling method is capable of achieving superior performance in terms of both visual quality and performance metrics with reference to recent methods. Most importantly, the downscaled images by our method do not suffer from edge blurring and texture loss, unlike many existing ones.